Federated Learning in Digital Health: Enhancing Privacy and Collaboration

Short description

Introduction

Federated learning (FL) is an emerging machine learning approach that enables collaborative model training across multiple institutions while ensuring data privacy. Unlike traditional centralized machine learning, FL allows data to remain local, addressing critical privacy concerns in digital health applications. This work explores FL’s impact on medical data analysis, predictive modeling, and personalized medicine while maintaining compliance with privacy regulations such as GDPR and HIPAA.

Methods

A federated learning framework was implemented and evaluated using publicly available healthcare datasets. The model was trained across distributed nodes representing multiple medical institutions without centralizing patient data. Key components include secure aggregation techniques, differential privacy mechanisms, and a comparison of FL with conventional machine learning models. The framework was tested in a practical scenario involving medical diagnostics to assess performance and privacy preservation.

Results

The federated model demonstrated competitive accuracy when compared to centralized approaches while preserving patient privacy. Performance metrics, including precision, recall, and computational efficiency, indicated that FL is a viable solution for large-scale medical data analysis. Privacy-enhancing techniques successfully mitigated data leakage risks, ensuring compliance with regulatory requirements.

Discussion and Impact on Medicine/Biology

The adoption of federated learning in healthcare has the potential to revolutionize data-driven medical research and clinical decision-making. By facilitating secure collaboration between institutions, FL enables broader access to high-quality training data without violating patient confidentiality. This approach can improve disease prediction models, accelerate drug discovery, and support real-time patient monitoring. Future research should focus on scalability, interoperability, and regulatory challenges to maximize FL’s impact on medical practice.

Conclusion

Federated learning presents a transformative opportunity for digital health, balancing data-driven insights with stringent privacy safeguards. The integration of FL into clinical workflows and telemedicine platforms can enhance personalized healthcare while ensuring compliance with ethical and legal standards.

Target Audience

Researchers, data scientists, healthcare professionals, and policy makers interested in artificial intelligence applications in medicine and privacy-preserving technologies.

Chair

Antoni Grzanka
e-mail: a.grzanka@ieee.org,
Medical University of Warsaw, Poland

Co-Chair

Radoslaw Roszczyk
e-mail: radoslaw.roszczyk@pw.edu.pl
Warsaw University of Technology, Poland

Program Committee

• Polish Section EMBS IEEE (EMB18)